Survey on Energy-Aware Cloud Computing Algorithms: A Review
DOI:
https://doi.org/10.26438/ijcse/v6i5.10951099Keywords:
energy efficiency, , vm-migration, load-balancing, vm-allocationAbstract
Cloud computing is an elastic model which is used to satisfied changing needs of users. It provides pay as you go services (PaaS, SaaS, and IaaS) to the users. The growing trend of cloud computing has raises the concern of energy efficiency in cloud computing because a data center consumes lots of energy and emits carbon-dioxide in the environment. Today, the main focus of researcher has been diverted from cloud resource management to energy management. Various algorithms on VM allocation, migration, task scheduling and load balancing have been developed to ensure minimum energy dissipation in cloud data center. The main focus of this paper is study the existing algorithms and to analysis the best algorithms
References
Beloglazov, A., & Buyya, R, “Energy efficient resource management in virtualized cloud data centers,” Proceedings of the IEEE/ACM international conference on cluster, cloud and grid computing, pp. 826-831,2010.
Arroba,P., et.al., “Dynamic Voltage and Frequency Scaling- Aware Dynamic Consolidation of Virtual Machines for Energy Efficient Cloud Data Center”, WILEY, 2016.
] Chien, N.k., et.al., “An Efficient Virtual Machine Migration Algorithm Based on Minimization of Migration in Cloud Computing”, International Conference on Nature of Computation and Communication ICTCC, Springer, pp. 62-71, 2016.
Tziritas, N et.al, “ Application Aware Workload Consolidations to Minimize both Energy Consumption and Network Load in Cloud Environment”, 42th International conference on Parallel Processing, IEEE, 2013.
Khan, M.A., et.al., “Dynamic Virtual Machine Consolidation Algorithms for Energy-Efficient Cloud Resource Management: A Review”, Springer International Publishing, pp.135-165,2018.
Beloglazov A, Abawajy J, Buyya R. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener Comput Syst. 2012;28(5):755–68.
Selim GEI, El-Rashidy MA, El-Fishawy NA, editors. An efficient resource utilization technique for consolidation of virtual machines in cloud computing environments. In: 2016 33rd national radio science conference (NRSC). 22–25 Feb 2016.
Beloglazov A, Abawajy J, Buyya R. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener Comput Syst. 2012;28(5):755–68.
Beloglazov A, Buyya R. Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr Comput. 2012;24(13):1397–420.
Abdi H. Multiple correlation coefficient. Richardson: The University of Texas at Dallas; 2007.
Verma A, Dasgupta G, Nayak TK, De P, Kothari R. Server workload analysis for power minimization using
consolidation. Proceedings of the 2009 USENIX Annual Technical Conference, San Diego, CA, USA, 2009; 28–28.
Gao,Y et.al., “A multi-objective ant colony system algorithm for virtual machine placement in cloud
computing”, Journal of Computer and System Sciences, Elsevier, 2013
Dabbagh M, Hamdaoui B, Guizani M, Rayes A. Toward energy-efficient cloud computing: Prediction, consolidation, and overcommitment. IEEE Netw. 2015;29(2):56–61.
Varasteh A, Goudarzi M. Server consolidation techniques in virtualized data centers: a survey. IEEE Syst J. 2015;11(2):772–83.
S. Martello, P. Toth, "Knapsack Problems–Algorithms and Computer Implementations", John Wiley & Sons, 1990
N. Tziritas, C.-Z. Xu, T. Loukopoulos, S. U. Khan, Z. Yu, "Application-aware Workload Consolidation to Minimize both Energy Consumption and Network Load in Cloud Environments", 42nd IEEE International Conference on Parallel Processing (ICPP), 2013
N. Quang-Hung, N. Thoai,, N. Son, "Epobf: Energy efficient allocation of virtual machines in high performance computing", Journal of Science and Technology, Vietnamese Academy of Science and Technology, Special on International Conference on Advanced Computing and Applications (ACOMP2013), Volume 51, pp 173-182, 2013
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